Evaluating a mammogram using a plurality of prior mammograms and deep learning algorithms

ABSTRACT

An approach for training, on a computer, one or more deep learning algorithms with a plurality of mammograms with known outcomes based, at least in part, on using a set of mammograms of each patient in the plurality of mammograms. The approach includes receiving a first set of mammograms of a first patient. The first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient. The approach includes the trained convolutional neural network extracting the set of features from each mammogram of the set of mammograms of the first patient. Furthermore, the approach includes using a second deep learning algorithm of the one or more deep learning algorithms to perform an evaluation of the unevaluated mammogram of the first patient based, at least in part, on the set of prior mammograms of the first patient.

BACKGROUND

The present invention relates generally to the field of computer dataprocessing, and more particularly to using multiple prior screeningexamination images with deep learning algorithms for classifying ascreening mammography image.

Implementation of national screening programs have significantly reducedthe risk of dying from some forms of cancer due to early diseasedetection. Reduced risk of death associated with breast cancer, coloncancer, and prostate cancer has been achieved. Improvements in screeningexamination image evaluations include the use of deep learningalgorithms for fine-grained recognition of anomalies and abnormalitiesusing various image classification methods in computer vision formedical imaging have contributed to the improvement in patient outcomes.

A conventional evaluation of a mammogram can be performed by aradiologist or physician. Additionally, trained deep learning algorithmsusing convolutional neural networks have been demonstrated to evaluate amammogram image. The evaluation of a mammogram using the trained deeplearning algorithms can be used by a physician or radiologist to provideadditional data in the radiologist's evaluation of a mammogram.Typically, a radiologist or physician may review the patient's historyincluding prior mammograms to aide in the radiologist's evaluation of amammogram.

SUMMARY

Embodiments of the present provide a method, a computer program product,and a computer system for training one or more second deep learningalgorithms with a plurality of mammograms with known outcomes based, atleast in part, on using a set of mammograms from each patient of aplurality of patients from a plurality of mammograms with knownoutcomes. The method includes one or more computer processors receivinga first set of mammograms of a first patient wherein, the first set ofmammograms includes an unevaluated mammogram of the first patient and aset of prior mammograms of the first patient. The method includes one ormore computer processors extracting, using a trained convolutionalneural network, a set of features from each mammogram of the set ofmammograms of the first patient. Furthermore, the method includes one ormore computer processors performing an evaluation of the unevaluatedmammogram of the first patient using the trained convolutional neuralnetwork and a trained deep learning algorithm of the one or more traineddeep learning algorithms based, at least in part, on the set of priormammograms of the first patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a functional block diagram of a computing environmentsuitable for operation of a multiple prior mammogram assessment program,in accordance with at least one embodiment of the invention.

FIG. 2A is a flow chart diagram depicting operational steps for themultiple prior mammogram assessment program using a convolutional neuralnetwork with random forest classifiers, in accordance with at least oneembodiment of the invention.

FIG. 2B is a block diagram illustrating multiple prior mammogramassessment program 225 using a convolutional neural network with arandom forest classifier, in accordance with at least one embodiment ofthe invention.

FIG. 3 is a flow chart diagram depicting operational steps for themultiple prior mammogram assessment program using a convolutional neuralnetwork with a modified long short-term memory algorithm, in accordancewith at least one embodiment of the invention.

FIG. 4 is an example of a program flow chart for a repeating module inthe modified LSTM algorithm for evaluating a mammogram using multipleprior mammograms, in accordance with at least one embodiment of theinvention.

FIG. 5 is a block diagram depicting components of a computer systemsuitable for executing the multiple prior mammogram assessment program,in accordance with at least one embodiment of the invention.

DETAILED DESCRIPTION

Embodiments of the present invention recognize that the use of deeplearning algorithms in the evaluation of mammograms is currently limitedto evaluating only a single, most recent mammogram without data from apatient's previous mammograms. Embodiments of the present inventionrecognize that the evaluation of a single mammogram using deep learningalgorithms, as currently practiced, is not as effective as an evaluationperformed by an experienced, medical professional. An experiencedmedical professional or radiologist who specializes reading mammogramscan use not only the current mammogram of a patient but also can reviewthe patient's previous mammograms and medical history to provide a moreaccurate assessment of whether the current mammogram is normal orabnormal than current deep learning algorithms as currently applied to asingle, current mammogram.

Embodiments of the present invention provide a method, a computerprogram, and a computer system using trained deep learning algorithmsevaluating a most recent mammogram of a patient based, at least in part,on using one or more prior mammograms of the patient. Embodiments of thepresent invention use a trained convolutional neural network (CNN) withone of a trained random forester (RF) classifier or a trained modifiedlong-short term memory (LSTM) algorithm for the evaluation of the mostrecent mammogram of a patient based, at least in part, on one or moreprior mammograms of the patient. Embodiments of the present inventionimprove the effectiveness of current deep learning algorithm evaluationsusing only a single, most recent mammogram by providing a method ofevaluating the most recent mammogram based, in part, on including anumber of prior mammograms of the patient in the evaluation that isperformed using a trained CNN and a second trained deep learningalgorithm that is one of a random forest classifier or a modified LSTMalgorithm.

Embodiments of the present invention present not only a method of usingmultiple prior mammograms of a patient in the evaluation of the mostrecent mammogram but, also, introduces a method of modifying atraditional LSTM algorithm to include a weighing factor associated withan image quality of each of the prior mammograms and a weighting factorassociated with a time elapsed from the most recent mammogram that isbeing evaluated to each prior mammogram.

The present invention will now be described in detail with reference tothe Figures.

FIG. 1 is a functional block diagram of a computing environment 100suitable for operation of multiple prior mammogram assessment (MPMA)program 225 in accordance with at least one embodiment of the invention.FIG. 1 provides only an illustration of one implementation and does notimply any limitations with regard to the environments in which differentembodiments may be implemented. Implementation of embodiments of theinvention may take a variety of forms, and exemplary implementationdetails are discussed subsequently with reference to the Figures. Manymodifications to the depicted environment may be made by those skilledin the art without departing from the scope of the invention as recitedby the claims.

Computing environment 100 includes computer 200, computer 300, andserver 400 with image database 450 connected over network 110. Network110 can be, for example, a local area network (LAN), a wide area network(WAN), such as the Internet, a virtual local area network (VLAN), or anycombination that can include wired, wireless, or optical connections. Ingeneral, network 110 can be any combination of connections and protocolsthat will support communications between computer 200, computer 300,server 400 and any other computing devices not depicted in FIG. 1.

As depicted, computer 200 is includes MPMA program 225, storage 250, anduser interface (UI) 260. In some embodiments, computer 200 can be adesktop computer system, a desktop computer, a laptop computer, aserver, a blade server, a tablet computer, a netbook computer, or anyother programmable electronic computing device capable of receiving,sending, and processing data, and communicating with features andfunctions of computer 300 and server 400 and other computing devices notshown within computing environment 100 via network 110. In anotherembodiment, computer 200 represents a computing system utilizingclustered computers and components (e.g., database server computers,application server computers, etc.) that act as a single pool ofseamless resources when accessed within distributed data processingenvironment 100. Computer 200 may include internal and external hardwarecomponents, as depicted in more detail and described in FIG. 5.

MPMA program 225 is depicted as operating on computer 200. In variousembodiments, computer 200 is a medical professional's computer. In anembodiment, MPMA program 225 operates on computer 200 that is a serverproviding mammogram evaluations to a number of client devices (e.g., toa number of medical personnel's or physician's computers). In oneembodiment, MPMA program 225 resides on computer 300 and receivesmammograms (e.g., mammogram image data) from imaging equipment 330 orstorage 350. MPMA program 225 provides an evaluation of a current ormost recent mammogram using both the current mammogram and a set ofprior mammograms associated with a patient. MPMA program 225 may receiveand send data to and from computer 300, image database 450 on server400, storage 250, and other computer devices not depicted withincomputing environment 100 using network 110.

For training of the deep learning algorithms (e.g., training of the CNN,RF classifier, and modified LSTM), MPMA program 225 can retrieve a largenumber of mammogram images with known, verified outcomes (e.g., averified as a normal or an abnormal mammogram by further testing oradditional mammograms, biopsies, ultrasounds, etc.) from one or more ofimage storage database 450, storage 350, or storage 250.

In various embodiments, MPMA program 225 uses a trained CNN and one of atrained RF classifier or a trained, modified LSTM algorithm to evaluatea current mammogram of a patient with a set of previous mammograms of apatient. A CNN can employ a mathematical operation called convolutionand consists of an input and an output layer, as well as multiple hiddenlayers where, in typical applications, the hidden layers of a CNNconsist of a series of convolutional layers that convolve with amultiplication or other dot product. In various embodiments, thetraining of either of the RF classifier or the modified LSTM in MPMAprogram 225 occurs after the CNN training. As known to one skilled inthe art, a LSTM is an artificial recurrent neural network architectureused in deep learning that has feedback connections (e.g., it processesnot only single data points or images, but also entire sequences of dataor images).

MPMA program 225, after training of the deep learning algorithms,receives a current, unevaluated mammogram of a patient and a set of oneor more prior mammograms from the same patient, for example, from one ormore of imaging equipment 330 in computer 300, from storage 350, fromstorage 250, or from image database 450 in server 400. MPMA program 225,using the trained deep learning algorithms (i.e., trained CNN and eitherthe trained RF or modified LSTM), can provide an evaluation of thecurrent mammogram. The result of the evaluation of the currentmammogram, for example, as normal or abnormal, may be displayed on UI260 to a physician.

Storage 250 is depicted in computer 200. Storage 250 may be any knowntype of storage and may include one or more databases. Storage 250 maystore mammograms, meta data or files associated with mammograms,evaluations of mammograms by MPMA program 225, any additionalinformation or data from another source (e.g., reports, ultrasound,biopsies, mammograms, etc.) from imaging equipment 330, database 450,other medical devices, or other computing devices (not depicted) incomputer environment 100.

UI 260 provides an interface to access the features and functions ofserver 400. In some embodiments, UI 260 provides to a user access toMPMA program 225 or to data in storage 250. UI 260 provides display ofoutput and input functions for computer 200. UI 250 enables respectiveusers of computer 200 to receive, view, hear, and respond to input,access applications, display content, and perform available functions.UI 260 may be any type of user interface capable of receiving a user'sinput to a program or a program's output. For example, UI 260 may be atouch screen display, a high definition display screen, a keyboard, anaudio output, an audio recording device with a voice recognition system,etc.

As depicted, computer 300 is a computing device including imagingequipment 330 capable of capturing mammogram images. Computer 300 may bea desktop computer, laptop computer, a tablet computer, or anyprogrammable electronic device capable of including or directlyconnecting to imaging equipment 330 and communicating with variouscomponents and devices within computer environment 100, via network 110.In general, computer 300 represents one or more programmable electronicdevices or a combination of programmable electronic devices capable ofexecuting machine-readable program instructions, imaging capabilities,and communicating with computer 200, server 400, and other computingdevices not shown within computer environment 100 via a network, such asnetwork 110. Computer 300, as depicted, includes imaging equipment 330,storage 350, and UI 360.

Imaging equipment 330 may be any known type of mammogram x-ray device orimaging device capable of providing mammograms or other medical images.Imaging equipment 330 can be capable of creating high quality images asmammograms using industry standard mammogram procedures. Imagingequipment 330 can capture and store captured images in storage 350.Imaging equipment 330 can be pre-programmed to send images to MPMAprogram 225 and image database 450.

In some embodiments, a program (not depicted) in computer 300 isincludes instructions to retrieve generated images, such as, mammogramsfrom storage 350. Computer 300 may include instructions to retrieveimages and send the retrieved images to one or more of MPMA program 225in computer 200, to image database 450, or to storage 350 when imagingoccurs or on a pre-determined time interval, such as, every hour, everyday, etc. In some embodiments, imaging equipment 330 is a standaloneimaging device automatically sending data, such as, mammogram images anddata associated with capturing mammograms over network 110 to one ormore of computer 300, server 400 or computer 200. Computer 300 mayinclude internal and external hardware components, depicted in moredetail in FIG. 5.

Storage 350 is depicted in computer 300. Storage 350 may be any knowntype of storage and may include one or more databases. Storage 350 maystore mammograms and mammogram metadata (e.g., time taken, resolution,technician, equipment model, etc.) from imaging equipment 330, anyadditional received evaluations from MPMA program 225, data from server400, or from other computing devices within computer environment 100. Invarious embodiments, storage 350 receives, retrieves, and sends data,such as, mammograms or data associated with mammograms to and fromimaging equipment 330, UI 360, computer 200, server 400, and othercomputing devices not depicted in computer environment over network 110.

UI 360 provides an interface to access the features and functions ofcomputer 300. UI 360 enables respective users of computer 300 toreceive, view, hear, and respond to input, access applications, displaycontent, and perform available functions on computer 300. In someembodiments, UI 360 provides instructions to imaging equipment 330. UI360 provides a user of computer 300 with the ability to display data andcommunicate instructions, data, images, and the like from programs andequipment associated with computer 300, computer 200, server 400 andother computing devices in computer environment 100. UI 360 includes thefunctions and capabilities of any standard UI, such as UI 260 above.

Server 400 is depicted as including image database 450. In someembodiments, server 130 can be a web server, a mainframe computer, adesktop computer, a laptop computer, a tablet computer, a netbookcomputer, or any other programmable electronic computing device capableof receiving, sending, and processing data, and communicating withfeatures and functions of computer 200, computer 300, and othercomputing devices (not shown) within computer environment 100 vianetwork 110. In another embodiment, server 400 represents a computingsystem utilizing clustered computers and components (e.g., databaseserver computers, application server computers, etc.) that act as asingle pool of seamless resources when accessed within distributed dataprocessing environment 100. Server 400 may include internal and externalhardware components, as depicted in more detail and described in FIG. 5.

Image database 450, depicted as residing in server 400, is a datarepository. Image database 450 can be one or more databases containingmammogram images, data associated with each mammogram image, such as, amammogram date taken, the type of equipment used for the mammogram(e.g., make, model, manufacturer, type of images, three dimensionalmammograms, image resolution, etc.), image evaluations, technician, andany other data provided to image database 450. In various embodiments,image database 450 retrieves, sends, and stores data from and tocomputer 200, computer 300, and other computing devices not depicted inFIG. 1.

FIG. 2 is a flow chart diagram depicting operational steps for trainingand using a trained MPMA program 225 when MPMA program 225 includes aCNN and a RF classifier, in accordance with at least one embodiment ofthe invention. The trained MPMA program 225 provides an output that isan outcome of the evaluation of the current or new mammogram of thepatient using a number of prior mammograms of a patient and the currentmammogram. Steps 202 through 206 discuss a method to train a CNN for usein MPMA program 225. Steps 208 through 220 discuss, in detail, thetraining of the RF classifier using a most recent mammogram of a patientand a set of prior mammograms of the patient. In training and aftertraining, the RF classifier uses the features extracted by the trainedCNN for each mammogram (e.g., the features extracted for each priormammogram and the most recent mammogram of a patient) as an input to theRF classifier.

In step 202, MPMA program 225 retrieves a plurality of mammograms withknown patient outcomes from one or more of image database 450, storage350 in computer 300, or storage 250 in computer 200 over network 110.The plurality of mammograms are taken from a large group of patients whohave provided a written consent allowing the use of mammograms andmammogram data to develop MPMA program 225.

For training of the various deep learning algorithms (e.g., a CNN, arandom forest classifier, or a modified LSTM), the prior evaluation ofeach mammogram of the plurality of retrieved mammograms is verified as acorrect evaluation (e.g., by further testing, biopsies, by latermammograms, etc.). The plurality of mammograms with known outcomesinclude many sets of one or more mammograms from the same patient and insome instances, may include only a single mammogram for some patients.The plurality of mammograms retrieved from image database 450 or anothersource. In various embodiments, the retrieved mammograms includeinformation, such as, a date a mammogram was taken, the equipment themammogram was taken on, image resolution of the mammogram, the nurse ormedical technician who captured the mammogram, the type of view, thearea captured, captured structural elements (e.g., muscles, etc.), whichbreast is captured, and other similar information relating to themammogram. The information associated with each mammogram may beembedded in the mammogram image data (e.g., metadata), on the image, orin a file associated with mammogram.

In step 204, MPMA program 225 receives a user selection of a CNNarchitecture. For example, with a selected CNN architecture, a set offeatures can be extracted from each retrieved mammogram from theplurality of mammograms with a verified previous evaluation. The numberof features extracted can be variable and dependent on the architectureof the CNN. For example, the set of features can be 128. In this case,the size of the feature vector, F_(c), of the current mammogram imagewill be 1×128.

In step 206, MPMA program 225 trains the CNN using the plurality ofmammograms retrieved by MPMA program 225. The training of the CNN occursby evaluating each of the plurality of mammograms with known outcomesusing conventional deep learning algorithm training methods. In variousembodiments, the training of the CNN occurs when MPMA program 225includes the CNN.

In one embodiment, the CNN is a separate program from MPMA program 225while under training. In this case, the CNN is trained separate fromMPMA program 225 and then, the trained CNN is incorporated into MPMAprogram 225 (e.g., the code and instructions of the trained CNN is addedto MPMA program 225).

The CNN evaluates only a single mammogram, based the set of featuresextracted from the image. The CNN does not include any prior mammogramsin training or in the evaluation of mammogram. A final layer or a set oflayers can be used to perform a prediction based on the extracted set offeatures. The training of the CNN occurs using the retrieved pluralityof mammograms with known outcomes. Based, at least in part, on a setextracted features of the mammogram, a final layer(s) of the CNN couldprovide an evaluation of a single, current, mammogram during training.The training of the CNN compares each CNN outcome to the known outcomeof the mammogram using conventional deep learning algorithm trainingmethodologies.

In step 208, MPMA program 225 receives a user selection of a number ofmammograms of each patient to be included in the evaluation of amammogram of the patient by MPMA program 225 using the RF classifier.The RF classifier in MPMA program 225 requires a fixed input size. Thenumber of mammograms utilized in MPMA program 225 using the trained CNNand a RF classifier can be set by a user. The number of mammogramsincludes the current mammogram for evaluation and a set of priormammograms of each patient.

In step 212, using the trained CNN, MPMA program 225 extracts the set offeatures for each mammogram of the set of mammograms for each patient.For the training of the RF classifier, the trained CNN inputs the set offeatures, for example, a set of 128 features for each mammogram of thenumber of mammograms for each patient. The trained CNN provides the setof features for a current mammogram being evaluated (i.e., with a knownoutcome) and a set of features for each of the prior mammograms for thepatient to the RF classifier. The total number of mammograms (e.g., thecurrent mammogram with the set of prior mammograms) matches the numberof mammograms defined for the RF classifier in step 208.

In the case of missing second prior mammogram 20C, the second priormammogram of the patient was not performed, or the mammogram data is notavailable. In this case, MPMA program 225 loads the feature vector,F_(p2), for missing second prior mammogram 20C with zeros.

In step 214, MPMA program 225 creates a feature vector concatenating theset of features extracted from each mammogram. From the plurality ofmammograms with known outcomes, MPMA program 225 creates a featurevector using the set of features extracted by the CNN for the currentmammogram being evaluated and for each of the prior mammograms of apatient. In an example illustrated in FIG. 2B, the set of features,F_(c) 22A, for current mammogram 20A, form elements Fc₁, Fc₂, Fc₃ toFc₁₂₈ in concatenated features 23 (a feature vector).

In some cases, a patient may not have the number of mammograms definedin step 208. When the number of mammograms for a patient does not meetthe defined number of mammograms to be used by the RF classifier, MPMAprogram 225 fills the set of feature vectors with zeros (e.g., MPMAprogram 225 has filled the set of features with zeros in step 208). Inthis way, the required number of mammograms defined in the RF classifiercan be maintained by MPMA program 225 even when a patient has not hadthe required number of mammograms. The second prior mammogram 20C ofFIG. 2B depicts an example of a patient with a missing mammogram wherethe feature vector F_(P2) is filled with zeros (not depicted in FIG. 2B)and the associated concatenated features are zeros.

In step 216, MPMA program 225 inputs the extracted set of features tothe RF classifier for each mammogram of a patient. MPMA program 225inputs the set of features and the concatenated features for the currentmammogram for each prior mammogram of a patient (e.g., from the set ofmammograms with known outcomes). The concatenated features can be inputinto the RF classifier. MPMA program 225 uses the concatenated featuresfor training the RF classifier using conventional deep learning trainingmethods.

In step 218, MPMA program 225 completes the training of the randomforest classifier using the concatenated features from the currentmammogram and the set of prior mammograms for each patients of a largenumber of patients from the plurality of mammograms with known outcomes.As depicted in FIG. 2B, when the training of the random forestclassifier is complete, output 25 of RF 24 should match the knownoutcome of current mammogram 20A.

In step 220, MPMA program 225 receives a current, unevaluated mammogramfor evaluation and a set of prior mammograms of a patient. For example,after training the CNN and training the RF classifier, MPMA program 225may receive, from computer 300, a current mammogram of a patientcaptured by imaging equipment 330 and a set of prior mammograms for thesame patient from storage 350.

In step 222, MPMA program 225 using the trained RF classifier andtrained CNN, performs an evaluation of the current mammogram of apatient based, at least in part, on the set of prior mammograms of thepatient. Using the trained deep learning algorithms with the currentmammogram and one or more prior mammograms of the patient, MPMA program225 may provide an evaluation or prediction of the patient's currentmammogram and the program ends. For example, MPMA program 225 mayprovide a binary output for output 25. Output 25 can be a binary outputsuch as “normal” or “recall” where recall indicates that furtherevaluation and/or monitoring of the patient is required (e.g., performanother mammogram in 6 months or perform a biopsy of identified abnormaltissue). In some cases, the binary output for output 25 may be“abnormal” or “normal” where “abnormal” also may indicate furthertesting or another screening examination of any identified abnormalitiessuch as, a cluster of malignant microcalcification in the examinedtissue. In other embodiments, MPMA program 225 output 25 is one or moreof a descriptive phrase, graph, or numerical output (e.g., a percent ofrisk, a percent normal tissue, etc.).

FIG. 2B is a block diagram illustrating MPMA program 225 using CNN 21with RF classifier 24, in accordance with at least one embodiment of theinvention. As depicted, FIG. 2B includes CNN 21, feature vectors, Fc22A, Fp₁ 22B, Fp₂ 22C, Fp₃ 22D, current mammogram 20A, first priormammogram 20B, missing second prior mammogram 20C, third prior mammogram20D, and concatenating features 23 along with RF 24 and output 25.Concatenating features 23 is a feature vector from the set of features(e.g., F_(C) 22A-F_(P3) 22D) determined by CNN 21. CNN 21 and RF 24 aretrained, for example, as discussed with respect to steps 202 to 206 ofFIG. 2A. In FIG. 2B, MPMA program 225 has received a set of mammogramsassociated with a patient in order for MPMA program 225 to provide anevaluation for output 25 of current mammogram 20A. The set ofmammograms, as depicted, include current mammogram 20A, first priormammogram 20B, missing second prior mammogram 20C, and third priormammogram 20D. As previously described, RF 24, requires a fixed inputsize, so since the second prior mammogram is missing, MPMA program 225will create a feature vector filled with zeros for missing second priormammogram 20C. MPMA program 225 as depicted in FIG. 2B, provides output25 of an evaluation of the current mammogram based, at least in, part ona set of prior mammograms for the same patient.

FIG. 3 is a flow chart diagram depicting operational steps for MPMAprogram 225 using a trained CNN with a modified LSTM, in accordance withat least one embodiment of the invention. After completing training ofthe CNN and the modified LSTM, MPMA program 225 evaluates a currentmammogram of a patient based, at least in part, on a set of priormammograms of the patient.

In step 302, MPMA program 225 retrieves a plurality of mammograms withknown patient outcomes for training the CNN and the modified LSTM. Insome embodiments, MPMA program 225 retrieves the plurality of mammogramsfrom image database 450 in server 400 using network 110. In otherembodiments, MPMA program 225 retrieves the plurality of mammograms fromone or more of storage 350 in computer 300, storage 250 in computer 200,another computer system storage or database in computer environment 100.

In step 304, MPMA program 225 receives a user selection of a CNNarchitecture. As discussed in detail with previously with reference tostep 204 of FIG. 2, using the selected CNN architecture, a set offeatures can be extracted from each retrieved mammogram from theplurality of mammograms with a verified previous evaluation.

In step 306, as previously discussed in detail with reference to step206 in FIG. 2, MPMA program 225 trains the CNN using the plurality ofmammograms retrieved by MPMA program 225. Using conventional methods,the training of the CNN occurs by evaluating each of the plurality ofmammograms with known outcomes.

In step 308, MPMA program 225 receives a user selection of a number ofmammograms to be evaluated in MPMA program 225. As known to one skilledin the art, a LSTM algorithm, unlike the RF classifier, can handle avariable number of inputs or mammograms. If a prior mammogram is missingwhen input into MPMA program 225 when using the modified LSTM, theprogram will not need to generate an input (e.g., zeros) for the missingmammogram. The number of mammograms includes the current mammogram forevaluation and a set of prior mammograms of each patient. For training,the current mammogram and the set of prior mammograms for training arefrom the plurality of mammograms with known outcomes.

In step 310, as previously discussed in detail with reference to step212, using the trained CNN, MPMA program 225 extracts the set offeatures for each mammogram of the number of mammograms for eachpatient. The extracted set of features from each mammogram in theplurality of mammograms with known outcomes can be input by MPMA program225 into the modified LSTM for training of the modified LSTM.

In step 312, MPMA program 225 completes the training of the modifiedLSTM using the sets of prior mammograms of a large number of patients.Using the sets of mammograms where each set of mammograms is associatedwith one patient. Each mammogram evaluated has a verified, known outcomefor training the modified LSTM (e.g., the mammograms used in trainingare from the plurality of mammograms with known outcomes).

During training the modified LSTM, the modified LSTM includes the numberof mammograms, if available, as defined in step 308, for a patient. Asknown to one skilled in the art, an LSTM sequentially processes eachreceived input in a stepwise approach. The input to the modified LSTM,at each sequence step or time point, is a mammogram where the oldestprior mammogram is input first in the modified LSTM and the currentmammogram is input last to the modified LSTM in MPMA program 225.

The evaluation of a most recent mammogram of the set of mammograms ofthe patient is based, at least in part, on the most recent mammogramsand the remaining mammograms in the set of mammograms of the patient. Intraining, the outcome of MPMA program 225 using the modified LSTM can becompared to an outcome of the most recent mammogram of the patient thathas been verified (e.g., by later mammograms, ultrasounds, etc.).

As opposed to a conventional LSTM, the modified LSTM assigns differentweights to different prior mammograms based, at least in part, on animage quality of each mammogram of the patient and on the time elapsedfrom the most recent mammogram under evaluation. For example, amammogram with higher image quality or a mammogram that is more recent(e.g., with less time elapsed from the mammogram under evaluation thanan older mammogram of the patient) can be given more weight in themodified LSTM.

The modified LSTM algorithm considers different features when assessingimage quality. For example, when evaluating image quality, the modifiedLSTM considers a combination of positioning and image clarity. Breastpositioning can determine if the appropriate landmarks or structuralelements (e.g., such as the pectoral muscle) are present in the image.Image clarity may determine an amount of blur in the image caused bymovement where images with blur or higher levels of blur would beconsidered low quality images. Other factors included by the modifiedLSTM in weighting image quality may be the level of contrast in themammogram. Older mammogram equipment or low compression of the breastduring testing may cause low contrast images. In the modified LSTM,mammograms with better image quality can be given more weight thanmammograms with lower image quality.

An example of a set of equations for a modified LSTM includesf_(t)=σ(W_(fh) h^(t-1)+W_(fx)*x^(t)+W_(fd)*δ^(t)+W_(fq)*q^(t)+b_(f)) asa first equation,i_(t)=σ(W_(ih)*h^(t-1)+W_(ix)*x^(t)+W_(id)*δ^(t)+W_(iq)*q^(t)+b_(i)) asa second equation,o_(t)=σ(W_(oh)*h^(t-1)+W_(ox)*x^(t)+W_(od)*δ^(t)+W_(oq)*q^(t)+b_(o)) asa third equation,c_(t)=σ(W_(ch)*h^(t-1)+W_(cx)*x^(t)+W_(cd)*δ^(t)+W_(cq)*q^(t)+b_(c)) asa fourth equation, h_(t)=f_(t)*h_(t-1)+i_(t)*c_(t) as a fifth equation,and y_(t)=o_(t)*tan h(h_(t)) as a sixth equation and where x^(t) is theinput vector, c_(t) is the long-term state, h_(t) is the short-termstate, f_(t) is the forget gate, i_(t) is the input gate, o_(f) is theoutput gate, δ^(t) is the time difference between the prior at time tand the current exam, q_(t) is the quality measure of the exam at timet, and y_(t) is the output.

Additionally, W_(fh), W_(ih), W_(oh), and W_(ch) are the weight matricesof each of the four layers for their connection to the previousshort-term state h^(t), W_(fx), W_(ix), W_(ox), and W_(cx) are theweight matrices of each of the four layers for their connection to theinput vector x^(t), W_(fd), W_(id), W_(od), and W_(cd) are the weightmatrices of each of the four layers for their connection to the timedifference between the prior at time t and the current exam δ^(t),W_(fg), W_(ig), W_(og), and W_(cg) are the weight matrices of each ofthe four layers for their connection to the quality measure of the examat time t, q^(t), and b_(f), b_(i), b_(o), and b_(c) are the bias termsfor each of the four layers.

The new elements in the modified LSTM algorithm depicted in the set ofequations above include, at least, the addition of q_(t), a measurementof each mammogram's image quality, and δ_(t), a time elapsed from themost recent mammogram under evaluation, W_(fd), W_(id), W_(od), andW_(cd), the weight matrices associated with the measure of imagequality, W_(iq), W_(fq), W_(iq), and W_(cq), the weight matricesassociated with the time elapsed from the most recent mammogram underevaluation. MPMA program 225 with the inclusion of a measure of imagequality of a mammogram and a measure of the time elapsed from performingthe most recent mammogram to the time of each prior mammogram is created(e.g., when the mammogram is performed) as weighting factors for eachprior mammogram of the patient provides an improvement in outcomes orevaluations of a most recent mammogram over an outcome generated by aconventional LSTM algorithm. As previously stated, using a conventionalLSTM would not include a consideration of previous mammograms, imagequality of each mammogram, and a time elapsed from the most recentmammogram in an evaluation the most recent mammogram.

In some embodiments, MPMA program 225 can be modified to detectasymmetry by modifications to the program to include views from both aleft side and a right side of the patient (e.g., a left breast and aright breast). The modifications to include mammograms from both sidesof the patient can incorporated in both the training of MPMA program 225(e.g., in the training of the CNN, RF classifier, and/or the modifiedLSTM) and in evaluations of a current mammogram after training. In theseembodiments, MPMA program 225 can be adjusted so that the input has twochannels rather than just one channel.

In step 314, MPMA program 225 receives a most recent mammogram and a setof prior mammograms of a patient. In various embodiments, MPMA program225 receives a most recent or current mammogram of the patient that hasnot been evaluated before from computer 300 over network 110. Eachmammogram of the set of prior mammograms of the patient can include atime stamp or meta data identifying a time taken and/or a model or dateof manufacture of the equipment used to capture the mammogram. In someexamples, the data on time taken and equipment used may be in a separatefile sent to MPMA program 225 or included as meta data with themammogram.

In step 316, MPMA program 225 using the trained CNN and modified LSTMevaluates the most recent mammogram based, at least in part, on the setof prior mammograms of the patient. In this case, MPMA program 225factors the image quality of each mammogram and the time elapsed fromthe most recent mammogram for each of the set of prior mammograms in themodified LSTM of MPMA program 225. As previously discussed, theevaluation of the most recent mammogram may be normal or abnormal but isnot limited to these specific evaluations or outcomes (e.g., may be anumerical output, a graphical output, etc.).

In one study of a number use cases with known outcomes, a comparison ofaccuracy of the outcomes (e.g., predictions) of the conventional methodof evaluation of a single mammogram using a trained CNN alone wascompared to the method using a trained CNN combined with a one of a RFclassifier or a modified LSTM trained with a number of prior mammogramsof the patient and the current mammogram of the patient. In thisexample, MPMA program 225 combines one of a trained RF classifier or atrained modified LSTM for evaluation of a current mammogram. In thisstudy, the trained CNN combined with the trained RF classifier or thetrained modified LSTM provided better results than the conventionallytrained CNN alone.

In this example, the study compares the receiver operatingcharacteristic (ROC) curve analysis of the outcomes of a conventionallytrained CNN (e.g., provides on outcome of a single mammogram aftertraining) and the method of MPMA program 225 using a conventionallytrained CNN with one of a trained RF classifier or a trained modifiedLSTM and a set of a patient's mammograms. For example, the area underthe ROC curve predicting whether a mammogram contains cancer for themodified LSTM in MPMA program 225 was 0.946, while the area under theROC curve using only a conventionally trained CNN evaluating a singlemammogram is 0.866. Using MPMA program 225 with a trained CNN and atrained RF classifier in MPMA program 225 resulted in an area under thecurve of 0.906.

The specificity at a sensitivity of 0.90% (e.g., related to the recallrate or a missed cancer diagnosis) for a single mammogram evaluatedusing a conventional CNN was seen as 0.671 while the specificity at asensitivity of 90% for MPMA program 225 with the RF classifier was 0.809and the MPMA program 225 with the modified LSTM was 0.871. Based theabove results, MPMA program 225 using either of an RF classifier or amodified LSTM when trained with multiple prior mammograms and a trainedCNN demonstrates an improvement in both the specificity and the areaunder the ROC curve over a conventional trained CNN that evaluates onlya current mammogram.

FIG. 4 is an example of a program flow chart for repeating module 40 ofthe modified LSTM algorithm, in accordance with at least one embodimentof the invention. As known to one skilled in the art, repeating module40 in the modified LSTM in includes four interacting layers (e.g., theforget gate controlling which parts of the long-term state should beerased, the input gate controlling which parts of the long-term stateshould be updated, the output gate controlling which parts of thelong-term state should be output at this time step, and the long-termstate). As depicted, FIG. 4 illustrates repeating module 40 at a time,t. Stepwise, over time, each repeating module 40 receives additionalinputs causing the long-term state to evolve as more and more inputs arereceived. Repeating module 40 can be also be known as a cell in themodified LSTM. The input to the LSTM model and the input to eachrepeating module 40 at each time point is a mammogram. The indexing,such as t−1, t, t+2, etc. keeps track of the prior mammograms which havebeen fed into the model sequentially. The prior mammograms are fed intothe model sequentially, by date, and then, the current mammogram is fedin last.

Repeating module 40 is an example of a step in the modified LSTMsequence occurring at time, t, where the next repeating module in thesequence (not depicted in FIG. 4) occurs at a time, t+1. The nextsequence of repeating module 40 at time, t+1, receives the outputs andlearned weights from repeating module 40 at time, t, as inputs.Similarly, repeating module 410 can receive inputs from a previousrepeating module 40 (e.g., previously occurring at time, t−1).

Repeating module 40, as depicted in FIG. 4, includes two new or novelelements not used in a repeating module of a traditional LSTM. Asdepicted in FIG. 4, repeating module 40 includes additional factorsδ^(t) 412 and q^(t) 414. δ^(t) 412 provides a weighting factor for atime elapsed from a current mammogram under evaluation and q^(t) 414provides a weighting factor for image quality. As known to one skilledin the art, each of 416 represents a layer in the modified LSTM, whilex_(t), σ, h_(t), c_(t), tan h, are standard variables as described abovewith reference to equation (1) and x, +, and tan h pointwise operands(e.g., ops). As depicted, FIG. 4 is one example of a repeating module inthe modified LSTM of MPMA program 225 when a modified LSTM isincorporated into MPMA program 225.

While embodiments of the present invention discuss MPMA program 225 as aprogram using trained deep learning algorithms to evaluate a current ormost recent mammogram, in other embodiments, MPMA program 225 canreceive examination screening images related to other types of medicalimaging procedures such as, cat scans or ultrasounds, for diagnosingother patient conditions. For example, images from ultrasounds,colonoscopies, lung tissue x-rays, prostrate images, or similar patientdiagnostic images can be evaluated using MPMA program 225 when trainedwith a same type of image (e.g., evaluate a current colonoscopy based ontraining using a plurality of colonoscopies with known outcomes). It isto be understood that while embodiments of the present invention mayrefer to the images as mammograms, MPMA program 225 is not limited toevaluations associated to mammograms but, MPMA program 225 can be usedto any number of other types of diagnostic images for patients (e.g.,cat scans, ultrasounds, lung x-rays, etc.). In an embodiment, somemodification of MPMA program 225 occur when MPMA program is applied withother types of images.

FIG. 5 is a block diagram depicting components of a computer system 500suitable for MPMA program 225, in accordance with at least oneembodiment of the invention. FIG. 5 displays the computer system 500,one or more processor(s) 504 (including one or more computer processorsor central processor units), a communications fabric 502, a memory 506including, a RAM 516, and a cache 518, a persistent storage 508, acommunications unit 512, I/O interfaces 514, a display 522, and externaldevices 520. It should be appreciated that FIG. 5 provides only anillustration of one embodiment and does not imply any limitations withregard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environment may be made.

As depicted, the computer system 500 operates over the communicationsfabric 502, which provides communications between the computerprocessor(s) 504, memory 506, persistent storage 508, communicationsunit 512, and input/output (I/O) interface(s) 514. The communicationsfabric 502 may be implemented with an architecture suitable for passingdata or control information between the processors 504 (e.g.,microprocessors, communications processors, and network processors), thememory 506, the external devices 520, and any other hardware componentswithin a system. For example, the communications fabric 502 may beimplemented with one or more buses.

The memory 506 and persistent storage 508 are computer readable storagemedia. In the depicted embodiment, the memory 506 comprises arandom-access memory (RAM) 516 and a cache 518. In general, the memory506 may comprise any suitable volatile or non-volatile one or morecomputer readable storage media.

Program instructions for MPMA program 225 may be stored in thepersistent storage 508, or more generally, any computer readable storagemedia, for execution by one or more of the respective computerprocessors 504 via one or more memories of the memory 506. In anembodiment, program instructions for MPMA program 225 may be stored inmemory 506. The persistent storage 508 may be a magnetic hard diskdrive, a solid-state disk drive, a semiconductor storage device, readonly memory (ROM), electronically erasable programmable read-only memory(EEPROM), flash memory, or any other computer readable storage mediathat is capable of storing program instruction or digital information.

The media used by the persistent storage 508 may also be removable. Forexample, a removable hard drive may be used for persistent storage 508.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of the persistentstorage 508.

The communications unit 512, in these examples, provides forcommunications with other data processing systems or devices. In theseexamples, the communications unit 512 may comprise one or more networkinterface cards. The communications unit 512 may provide communicationsthrough the use of either or both physical and wireless communicationslinks. In the context of some embodiments of the present invention, thesource of the various input data may be physically remote to thecomputer system 500 such that the input data may be received, and theoutput similarly transmitted via the communications unit 512.

The I/O interface(s) 514 allow for input and output of data with otherdevices that may operate in conjunction with the computer system 500.For example, the I/O interface 514 may provide a connection to theexternal devices 520, which may be as a keyboard, keypad, a touchscreen, or other suitable input devices. External devices 520 may alsoinclude portable computer readable storage media, for example thumbdrives, portable optical or magnetic disks, and memory cards. Softwareand data used to practice embodiments of the present invention may bestored on such portable computer readable storage media and may beloaded onto the persistent storage 508 via the I/O interface(s) 514. TheI/O interface(s) 514 may similarly connect to a display 522. The display522 provides a mechanism to display data to a user and may be, forexample, a computer monitor.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disk read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adaptor card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, though the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for exampleprogrammable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a readable storage medium that can direct acomputer, a programmable data processing apparatus, and/or other devicesto function in a particular manner, such that the computer readablestorage medium having instructions stored therein comprises an articleof manufacture including instructions which implement aspects of thefunction/act specified in the flowchart and/or block diagram blocks orblocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof computer program instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the block may occurout of the order noted in the Figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A computer-implemented method, thecomputer-implemented method comprising: training, by one or morecomputer processors, one or more deep learning algorithms based, atleast in part, on using a set of mammograms of each patient of aplurality of patients from a plurality of mammograms with knownoutcomes; receiving, by one or more computer processors, a first set ofmammograms for a first patient wherein, the first set of mammogramsincludes an unevaluated mammogram of the first patient and a set ofprior mammograms of the first patient; extracting, by one or morecomputer processors, by a trained convolutional neural network, a set offeatures from each mammogram in the first set of mammograms of the firstpatient; and performing, by one or more computer processors, anevaluation of the unevaluated mammogram of the first patient using thetrained convolutional neural network and a second trained deep learningalgorithm based, at least in part, on the set of prior mammograms of thefirst patient.
 2. A computer-implemented method of claim 1, training theone or more deep learning algorithms based, at least in part, on usingthe set of mammograms of each patient of the plurality of patients fromthe plurality of mammograms with the known outcomes, further comprises:extracting, by one or more computer processors, by the trainedconvolutional neural network, the set of features from each mammogram ofa plurality of mammograms with the known outcomes; and providing, by oneor more computer processors, the set of features extracted by thetrained convolutional neural network to a deep learning algorithm of theone or more deep learning algorithms.
 3. A computer-implemented methodof claim 1, further comprises providing an output of the evaluation ofthe unevaluated mammogram of the first patient wherein, the outputincludes one or more of a graphical output, a numerical output, awritten output, or a binary outcome.
 4. A computer-implemented method,the computer-implemented method comprising: extracting, by one or morecomputer processors, by a trained convolutional neural network, a set offeatures from each mammogram of a plurality of mammograms with knownoutcomes; training, by one or more computer processors, a modifiedlong-short term memory algorithm based, at least in part, on a set ofmammograms of each patient in the plurality of mammograms with the knownoutcomes; receiving, by one or more computer processors, a first set ofmammograms of a first patient wherein, the first set of mammogramsincludes an unevaluated mammogram of the first patient and a set ofprior mammograms of the first patient; extracting, by one or morecomputer processors, by the trained convolutional neural network, theset of features from each mammogram of the first set of mammograms ofthe first patient; and performing, by one or more computer processors,an evaluation of the unevaluated mammogram of the first patient usingthe trained convolutional neural network and the trained modifiedlong-short term memory algorithm based, at least in part, on the firstset of mammograms of the patient.
 5. A computer-implemented method ofclaim 4, wherein training the modified long-short term algorithmincluding providing one or more weighting factors associated with eachmammogram of the set of mammograms of each patient wherein, the one ormore weighting factors modify a long-short term memory algorithm.
 6. Acomputer-implemented method of claim 5, wherein an image quality of eachmammogram of the set of mammograms of each patient is a weighting factorof the one or more weighting factors associated with each mammogram ofthe set of mammograms of each patient.
 7. A computer-implemented methodof claim 6, wherein the image quality of each mammogram of the set ofmammograms of each patient is based on a level of contrast in eachmammogram, structural elements of a breast in each mammogram, and animage clarity.
 8. A computer-implemented method of claim 6, wherein afirst mammogram of the set of mammograms with better image quality isgiven more weight than a second mammogram of the set of mammograms witha lower image quality image.
 9. A computer-implemented method of claim5, wherein training the modified long-short term algorithm includes aweighting factor associated with a time elapsed from a current mammogrambeing evaluated to each prior mammogram in the set of mammograms of eachpatient wherein, a more recent mammogram in the set of mammograms ofeach patient with less time elapsed from the current mammogram beingevaluated is given more weight than an older mammogram of the set ofmammograms of each patient with more elapsed time from the currentmammogram.
 10. A computer-implemented method, the computer-implementedmethod comprising: extracting, by one or more computer processors, by atrained convolutional neural network, a set of features from eachmammogram of a plurality of mammograms with known outcomes; training, byone or more computer processors, a random forest classifier with theplurality of mammograms with the known outcomes based, at least in part,on using a set of mammograms of each patient of a plurality of patientsin the plurality of mammograms with the known outcomes; receiving, byone or more computer processors, a first set of mammograms of a firstpatient wherein, the first set of mammograms includes an unevaluatedmammogram of the first patient and a set of prior mammograms of thefirst patient; extracting, by one or more computer processors, by thetrained convolutional neural network, the set of features from eachmammogram of the first set of mammograms of the first patient; andperforming, by one or more computer processors, an evaluation of theunevaluated mammogram of the first patient using a trained random forestclassifier based, at least in part, on the set of prior mammograms ofthe first patient.
 11. The computer-implemented method of claim 10,wherein training the random forest classifier with the plurality ofmammograms with the known outcomes, further comprises: receiving, by oneor more computer processors, a user input of a number of mammogramsneeded for the set of mammograms of each patient of the plurality ofpatients in the training of the random forest classifier; creating, byone or more computer processors, a feature vector concatenating eachfeature of the set of features from each mammogram of the plurality ofmammograms with the known outcomes; determining, by one or more computerprocessors, that one or more mammograms of the number of mammogramsneeded for the set of mammograms of each patient of the plurality ofpatients in the training of the random forest classifier is missing; andinputting, by one or more computer processors, zeros for each feature ofthe set of features associated with the one or more missing mammograms.12. A computer program product comprising: one or more computer readablestorage media and program instructions stored on the one or morecomputer readable storage media, the program instructions executable bya processor, the program instructions comprising instructions for:extracting, by one or more computer processors, by a trainedconvolutional neural network, a set of features from each mammogram of aplurality of mammograms with known outcomes; training, by one or morecomputer processors, one or more deep learning algorithms based, atleast in part, on using a set of mammograms of each patient of aplurality of patients from the plurality of mammograms with the knownoutcomes; receiving, by one or more computer processors, a first set ofmammograms for a first patient wherein, the first set of mammograms ofthe first patient includes an unevaluated mammogram of the first patientand a set of prior mammograms of the first patient; extracting, by oneor more computer processors, by the trained convolutional neuralnetwork, the set of features from each mammogram of the first set ofmammograms of the first patient; and performing, by one or more computerprocessors, an evaluation of the unevaluated mammogram of the firstpatient using the trained convolutional neural network and a deeplearning algorithm of the one or more trained deep learning algorithmsbased, at least in part, on the set of prior mammograms of the firstpatient.
 13. The computer program product of claim 12, wherein, trainingthe one or more deep learning algorithms based, at least in part, onusing the set of mammograms of each patient of the plurality of patientsfrom the plurality of mammograms with the known outcomes includestraining one of a random forest classifier or a modified long-short termmemory algorithm after the trained convolutional neural network.
 14. Thecomputer program product of claim 12, further comprises providing anoutput of the evaluation of the unevaluated mammogram of the firstpatient wherein, the output includes one or more of a graphical output,a numerical output, a written output, or a binary outcome.
 15. Thecomputer program product of claim 14, wherein the binary outcomeincludes one of normal or abnormal.
 16. The computer program product ofclaim 13, wherein training the modified long-short term algorithmincludes providing one or more weighting factors associated with eachmammogram of the set of mammograms of each patient wherein, the one ormore weighting factors modify a long-short term memory algorithm. 17.The computer program product of claim 16, wherein an image quality ofeach mammogram of the set of mammograms of each patient is a weightingfactor of the one or more weighting factors associated with eachmammogram of the set of mammograms of each patient.
 18. The computerprogram product of claim 16, wherein training the modified long-shortterm algorithm includes a weighting factor associated with a timeelapsed from a current mammogram being evaluated to each prior mammogramin the set of mammograms of each patient wherein, a more recentmammogram in the set of mammograms with less time elapsed from thecurrent mammogram being evaluated is given more weight than an oldermammogram.
 19. The computer program product of claim 13, whereintraining the one or more deep learning algorithms based, at least inpart, on using the set of mammograms of each patient of the plurality ofpatients from the plurality of mammograms with the known outcomes,further comprises: training, by one or more computer processors, arandom forest classifier as a deep learning algorithm of the one or moredeep learning occurs using the trained convolutional neural network;receiving, by one or more computer processors, the first set ofmammograms of the first patient wherein, the first set of mammograms ofthe first patient includes the unevaluated mammogram of the firstpatient and the set of prior mammograms of the first patient;extracting, by one or more computer processors, by the trainedconvolutional neural network, the set of features from each mammogram ofthe first set of mammograms of the first patient; and performing, by oneor more computer processors, the evaluation of the unevaluated mammogramof the first patient using the trained random forest classifier based,at least in part, on the set of prior mammograms of the first patient.20. The computer program product of claim 13, wherein training therandom forest classifier further comprises: receiving, by one or morecomputer processors, a user input of a number of mammograms needed forthe set of mammograms of each patient of the plurality of patients inthe training of the random forest classifier; creating, by one or morecomputer processors, a feature vector concatenating each feature of theset of features from each mammogram of the plurality of mammograms withthe known outcomes; determining, by one or more computer processors,that one or more mammograms of the number of mammograms needed for theset of mammograms of each patient of the plurality of patients in thetraining of the random forest classifier is missing; and inputting, byone or more computer processors, zeros for each feature of the set offeatures associated with the one or more missing mammograms.
 21. Acomputer system comprising: one or more computer processors; one or morecomputer readable storage media; program instructions stored on the oneor more computer readable storage media for execution by at least one ofthe one or more processors, the program instructions comprisinginstructions to perform: extracting, by one or more computer processors,by a trained convolutional neural network, a set of features from eachmammogram of a plurality of mammograms with known outcomes; training, byone or more computer processors, one or more deep learning algorithmsbased, at least in part, on using a set of mammograms of each patient ofa plurality of patients from the plurality of mammograms with the knownoutcomes; receiving, by one or more computer processors, a first set ofmammograms for a first patient wherein, the first set of mammogramsincludes an unevaluated mammogram of the first patient and a set ofprior mammograms of the first patient; extracting, by one or morecomputer processors, by the trained convolutional neural network, theset of features from each mammogram of the first set of mammograms ofthe first patient; and performing, by one or more computer processors,an evaluation of the unevaluated mammogram of the first patient usingthe trained convolutional neural network and a trained deep learningalgorithm of the one or more trained deep learning algorithms based, atleast in part, on the set of prior mammograms of the first patient. 22.The computer system of claim 21, wherein, training the one or more deeplearning algorithms based, at least in part, on using the set ofmammograms of each patient of the plurality of patients from theplurality of mammograms with the known outcomes includes training one ofa random forest classifier or a modified long-short term memoryalgorithm.
 23. The computer system of claim 22, wherein training themodified long-short term algorithm includes providing one or moreweighting factors associated with each mammogram of the set ofmammograms of each patient wherein, the one or more weighting factorsmodify a long-short term memory algorithm.
 24. The computer system ofclaim 23, wherein an image quality of each mammogram of the set ofmammograms of each patient is a weighting factor of the one or moreweighting factors associated with each mammogram of the set ofmammograms of each patient.
 25. The computer system of claim 23, whereintraining the random forest classifier further comprises: receiving, byone or more computer processors, a user input of a number of mammogramsneeded for the set of mammograms of each patient of the plurality ofpatients in the training of the random forest classifier; creating, byone or more computer processors, a feature vector concatenating eachfeature of the set of features from each mammogram of the plurality ofmammograms with the known outcomes; determining, by one or more computerprocessors, that one or more mammograms of the number of mammogramsneeded for the set of mammograms of each patient of the plurality ofpatients in the training of the random forest classifier is missing; andinputting, by one or more computer processors, zeros for each feature ofthe set of features associated with the one or more missing mammograms.